CN112534236B

CN112534236B - Abnormality diagnosis device and abnormality diagnosis method

Info

Publication number: CN112534236B
Application number: CN201880096289.8A
Authority: CN
Inventors: 田中康裕
Original assignee: Nissan Motor Co Ltd
Current assignee: Nissan Motor Co Ltd
Priority date: 2018-08-06
Filing date: 2018-08-06
Publication date: 2023-03-24
Anticipated expiration: 2038-08-06
Also published as: JPWO2020031225A1; CN112534236A; US20210178615A1; EP3835750A1; JP7079420B2; EP3835750A4; WO2020031225A1

Abstract

An abnormality diagnosis device that diagnoses an abnormality of each reduction gear (14) based on disturbance torque related to a state of the reduction gear (14) acquired from a sensor (13) provided in a robot (101) having a plurality of reduction gears (14), and displays a result of the diagnosis on a display (62), the abnormality diagnosis device comprising: a maintenance history DB (32) that stores maintenance data relating to maintenance performed on each of the reducers (14); and a control unit (51) that detects an abnormality of each reduction gear (14) on the basis of the disturbance torque. When an abnormality of the speed reducer (14 a) is detected based on the disturbance torque, the control unit (51) predicts an abnormality of the speed reducer (14 b) that occurs in association with the abnormality of the speed reducer (14 a) based on the maintenance data, and displays information relating to the predicted abnormality on the display (62).

Description

Abnormality diagnosis device and abnormality diagnosis method

Technical Field

The present invention relates to an abnormality diagnosis device and an abnormality diagnosis method for diagnosing an abnormality occurring in a movable portion such as a speed reducer provided in a device such as a robot.

Background

Conventionally, as a device for detecting an abnormality occurring in a device, for example, a device disclosed in patent document 1 is known. Patent document 1 discloses an apparatus that: the date and time when an abnormality occurs in the device to be diagnosed, sound data, whether the diagnosis result is correct, and the record of work (check, problem, time and place of execution) are recorded in association with each other, and a case where the sound when an abnormality occurs in the device is similar to that in the past is presented to the user.

Documents of the prior art

Patent document

Patent document 1: japanese patent laid-open No. 2005-339142

Disclosure of Invention

However, the above-mentioned patent document 1 has the following problems: when an abnormality occurs in a device to be diagnosed, a causal relationship with an abnormality in another device cannot be known.

The present invention has been made to solve the above-described conventional problems, and an object thereof is to provide an abnormality diagnosis apparatus and an abnormality diagnosis method capable of predicting an abnormality of one movable unit and presenting the predicted abnormality to an operator when an abnormality occurs in another movable unit.

One embodiment of the present invention includes: a maintenance history storage unit that stores maintenance data relating to maintenance performed on each movable unit; and a control unit for diagnosing an abnormality of each movable unit. When an abnormality of one movable unit is detected based on the movable unit data, the control unit predicts an abnormality of another movable unit occurring in association with the abnormality of the one movable unit based on the maintenance data, and outputs information on the predicted abnormality of the other movable unit to the display unit.

ADVANTAGEOUS EFFECTS OF INVENTION

According to one aspect of the present invention, when an abnormality occurs in one movable section, it is possible to predict an abnormality in another movable section associated therewith and present the predicted abnormality to the operator.

Drawings

Fig. 1 is a block diagram showing the configuration of an abnormality diagnostic device and its peripheral devices according to an embodiment of the present invention.

Fig. 2 is an explanatory diagram showing an example of the abnormality diagnostic apparatus shown in fig. 1, which is an integral computing means.

Fig. 3 is a flowchart showing a processing procedure of the correlation analysis processing performed by the abnormality diagnostic device according to the present invention.

Fig. 4 is a flowchart showing a processing procedure of the abnormality detection apparatus according to the present invention.

Fig. 5 is a graph showing changes in disturbance torque and abnormality degree.

Fig. 6 is an explanatory diagram showing an implementation of an abnormality and maintenance detected in each reduction gear.

Fig. 7A is an explanatory diagram showing the maintenance history a.

Fig. 7B is an explanatory diagram showing the maintenance history B.

Fig. 8 is an explanatory diagram showing a tree image of the passage of abnormality diagnosis.

Fig. 9 is an explanatory diagram showing an abnormality diagnostic image displayed on the display.

Fig. 10 is an explanatory diagram showing an abnormality diagnostic image displayed on the display, showing an example in which the maintenance history a is displayed enlarged with respect to the maintenance history B.

Fig. 11 is a time chart showing maintenance (H1 to Hn) performed in each reduction gear and an abnormality detected in the reduction gear.

Detailed Description

Embodiments of the present invention will be described below with reference to the drawings.

[ description of the first embodiment ]

Fig. 1 is a block diagram showing the configuration of an abnormality diagnostic device and its peripheral devices according to an embodiment of the present invention. As shown in fig. 1, an abnormality diagnosis device 102 according to the present embodiment is connected to a robot 101 (device) and a user interface 103 (denoted as "UI" in the drawing) to diagnose an abnormality of the robot 101, outputs data related to a diagnosis result to a display 62 (display unit) provided in the user interface 103, and displays the diagnosis result on the display 62. The term "diagnosing an abnormality" is a concept including not only determination of an abnormality that is currently occurring but also prediction of an abnormality that will occur in the future.

The robot 101 is a teaching playback type multi-axis robot, for example. The teaching playback represents the following functions: the operator actually operates the robot using a teach pendant attached to the robot, records and reproduces the operation of the robot, and operates the robot. In the present embodiment, a teaching playback type robot is taken as an example for explanation, but the present invention is not limited to this.

As shown in fig. 1, the robot 101 includes a speed reducer 14 (movable unit), an operation control unit 15, a sensor 13, a disturbance torque calculation unit 12, and a communication unit 11. Although the robot 101 is provided with a plurality of speed reducers 14, fig. 1 shows only one speed reducer 14.

The decelerator 14 includes a servo motor (hereinafter, simply referred to as a "motor") for operating a joint shaft of the robot arm, and operates under the control of the operation control unit 15. Then, by operating the speed reducer 14, for example, a welding electrode (welding portion) mounted on the tip end of the robot arm is brought into contact with a desired portion of an object to be processed (for example, a metal blank), thereby performing a welding operation. In addition to the welding operation, various operations such as pressing, painting, resin molding, and assembling of the object can be performed by the robot 101.

The sensor 13 is provided in the robot 101, and includes, for example, a pulse generator, an encoder, and the like, and detects various physical quantities such as a position and an angle of the robot arm operated by the decelerator 14, a rotation angle, a rotation speed, power consumption, and current of a motor provided in the decelerator 14, and a rotation angle of the decelerator 14. The sensor 13 also detects a torque value generated in the motor of the reducer 14. The sensor data detected by the sensor 13 is transmitted from the communication unit 11 to the abnormality diagnostic device 102.

The operation control unit 15 controls the speed reducer 14 to operate according to the operation program set by the teaching described above, so that each robot arm and joint shaft mounted on the robot 101 perform a desired operation. Then, the operation data when the robot 101 is operated is output to the communication unit 11. The work data includes various information related to the work of the robot 101. The details will be described later.

The disturbance torque calculation unit 12 calculates disturbance torque generated in the motor of the reduction gear 14. The disturbance torque represents a difference between a torque command value at the time of controlling the motor and a torque detection value detected by the sensor 13. When the motor is normal and the reduction gear 14 is stably operated, the difference between the torque command value and the torque detection value is substantially constant, and therefore the disturbance torque shows a stable value. When an abnormality occurs in the speed reducer 14, the speed reducer 14 cannot operate stably, and the disturbance torque changes greatly. The disturbance torque is an example of movable portion data related to the state of the movable portion (reduction gear 14).

The communication unit 11 transmits the operation data of the robot 101, the disturbance torque calculated by the disturbance torque calculation unit 12, and various sensor data detected by the sensor 13 to the abnormality diagnosis device 102.

Each of the above functions of the robot 101 can be realized by one or more processing circuits. The processing circuit includes a programmed processing device such as a processing device including an electrical circuit. The processing device also includes devices such as Application Specific Integrated Circuits (ASICs) and existing circuit components that are laid out to execute the functions of the robot 101.

The user interface 103 includes: a display 62 that displays various information; and a display control unit 61 that controls display of various information transmitted from the abnormality diagnostic device 102. An input unit 63 is further provided for the operator to perform various operations. When maintenance data indicating that the operator has performed maintenance on the robot 101 is input to the input unit 63, the maintenance data is written into a maintenance history DB32 (maintenance history storage unit) described later. The user interface 103 can also use a tablet terminal or the like.

Next, the configuration of the abnormality diagnostic device 102 will be described. The abnormality diagnostic device 102 includes a control unit 51 and various Databases (DBs). The control unit 51 includes a communication unit 21, an abnormality degree determination unit 22, an abnormality prediction unit 23, a notification content setting unit 24, and a correlation analysis unit 25. The database includes a sensor DB 31, a maintenance history DB32 (maintenance history storage section), an abnormality prediction DB 33, a correlation storage DB 34 (correlation storage section), and a work history DB 35.

The sensor DB 31 stores sensor data such as the position and angle of the robot arm, the rotation angle and the rotation speed of the motor, which are detected by the sensor 13. The disturbance torque (data on the state of the movable unit) calculated by the disturbance torque calculation unit 12 is also stored.

The work history DB 35 stores work data of the robot 101. The operation data includes various data related to operations such as the date of operation of the robot 101, the time at which the operation is started, the time at which the operation is stopped, the time during which the operation is continued, and the time during which the operation is continuously stopped. The operation data includes an operation mode of the reduction gear 14. The operation modes include a normal operation mode, a maintenance mode, and a stop mode.

When an abnormality occurs in the speed reducer 14 or when it is predicted that an abnormality will occur, the maintenance history DB32 stores maintenance data when the robot 101 is subjected to maintenance. The maintenance data can be input by an operator through the input section 63 of the user interface 103. Alternatively, when the robot 101 is operated in the maintenance mode described above, it may be determined that maintenance is being performed, and maintenance data may be automatically created and stored. The maintenance data includes the ID number of the reducer 14 subjected to maintenance, the ID number of a motor mounted on the reducer 14, the date and time when the maintenance was performed, and the contents of the maintenance (replacement, repair, grease replacement, etc.).

The correlation storage DB 34 stores a correlation (details will be described later) derived by the correlation analysis unit 25 described later. The correlation memory DB 34 also stores the correspondence between one speed reducer provided in the robot 101 and another speed reducer that operates in conjunction with the same speed reducer. For example, the robot 101 is provided with 6 reducers 14 (these are

reducers

14a, 14b, 14c, 14d, 14e, and 14 f), and when the reducer 14a operates in conjunction with the reducer 14b and the reducers 14d, 14e, and 14f operate in conjunction, the correspondence relationship between these is stored. The phrase "the reduction gear 14a operates in conjunction with the reduction gear 14b" means a concept including a case where the operation of the reduction gear 14a has some influence on the operation of the reduction gear 14b. In the following description, when each reduction gear is specifically shown, a suffix is added to the illustration as in the case of "reduction gear 14a" or "reduction gear 14b", and when not specifically shown, the illustration is "reduction gear 14".

The abnormality prediction DB 33 stores abnormality prediction data predicted by the abnormality prediction unit 23 (described later in detail). For example, when an abnormality is detected based on disturbance torque of a certain reduction gear 14a and an abnormality occurring in the reduction gear 14b operating in conjunction with the reduction gear 14a is predicted, the predicted data is stored as abnormality prediction data.

The communication unit 21 communicates with the communication unit 11 provided in the robot 101. The communication unit 21 receives the operation data of the robot 101 transmitted from the robot 101 and outputs the operation data to the operation history DB 35. The communication unit 21 receives the disturbance torque and the sensor data transmitted from the robot 101, and outputs them to the sensor DB 31.

The correlation analysis unit 25 performs correlation analysis for the abnormality occurring in each reduction gear 14 based on the maintenance data of each reduction gear 14 stored in the maintenance history DB 32. Fig. 6 shows an example of the maintenance history of each reduction gear 14, and the horizontal axis indicates the time from 1 week to several weeks. In fig. 6, "o" indicates an abnormality occurring in the speed reducer 14, "Δ" indicates that maintenance for replacing grease is performed, and "9633;" indicates that maintenance for replacing the speed reducer 14 is performed. As shown in fig. 6, in each of the

speed reducers

14a, 14b, and 14c, if grease is replaced after an abnormality occurs, the speed reducer 14 is replaced at a high frequency within a few days thereafter. Therefore, it is determined that there is a correlation between the replacement of grease and the subsequent replacement of the reduction gear 14. The correlation relationship obtained by the correlation analysis is stored in the correlation storage DB 34.

The abnormality degree determination unit 22 acquires past disturbance torque of the motor mounted on the speed reducer 14 from the sensor DB 31, and calculates an abnormality degree indicating an abnormality of the acquired disturbance torque. Hereinafter, a method of calculating the degree of abnormality will be described. The abnormality degree a (x ') when the disturbance torque is x' is defined by the following formula (1).

a(x’)＝{(x’-m) ² }/2·s ² …(1)

Where m is the sample average of the disturbance torque and s is the standard deviation of the disturbance torque.

When the abnormality degree a (x') exceeds a preset reference value, it is determined that the disturbance torque is abnormal. Fig. 5 (a) shows a waveform of disturbance torque generated in the speed reducer 14a, and the abnormality degree is calculated by the above equation (1) based on the disturbance torque shown in fig. 5 (a). As a result, the degree of abnormality shown in fig. 5 (b) is calculated. For example, at the symbol p1 in fig. 5 (b), the abnormality degree exceeds the reference value (1.0), and it is determined that the disturbance torque is abnormal.

In addition to the above methods, as a method of calculating the degree of abnormality, kernel density estimation, density ratio estimation, or the like can be used. As another method of calculating the degree of abnormality, a difference between the disturbance torque and a predetermined value is calculated, and a rate of change of the difference with respect to the passage of time is calculated. Further, it is also possible to determine that the disturbance torque is abnormal when the calculated change rate exceeds a predetermined threshold value. The predetermined value may be an average value of disturbance torques in the same month 1 year ago, for example.

The abnormality prediction unit 23 determines or predicts an abnormality occurring in each reduction gear 14 based on the abnormality degree calculated for each reduction gear 14. When it is determined that the degree of abnormality of a certain reduction gear 14a is high based on the degree of abnormality calculated by the abnormality degree determination unit 22, it is determined that the reduction gear 14a is abnormal. Then, the list of the speed reducers 14 operating in conjunction with the speed reducer 14a is acquired with reference to the correlation storage DB 34. In the present embodiment, the reduction gear 14b is obtained as the above list. Although there may be a plurality of speed reducers 14 that operate in conjunction with the speed reducer 14a, the description will be given here only with reference to the speed reducer 14b as an example.

The abnormality prediction unit 23 detects an abnormality occurring in the

reduction gears

14a, 14b based on the maintenance data (maintenance timing, contents of maintenance, etc.) of the

reduction gears

14a, 14b stored in the maintenance history DB32 and the correlation stored in the correlation storage DB 34.

For example, as shown in fig. 6, if an abnormality of a certain speed reducer 14 is detected, then grease replacement is performed, and then maintenance work for replacing the speed reducer 14 is performed at a high frequency. It is determined that there is a correlation between the replacement of grease and the subsequent replacement of the reduction gear 14. Therefore, when the abnormality is detected in the reduction gear 14a after the grease replacement is performed in the reduction gear 14b, it is determined that there is a high possibility that the abnormality requiring the replacement of the reduction gear 14 occurs in the reduction gear 14b, and therefore it is predicted that the abnormality occurs in the reduction gear 14b. That is, when an abnormality is detected in the speed reducer 14a (one movable unit), the abnormality prediction unit 23 refers to the correlation stored in the correlation storage DB 34 to predict an abnormality of the speed reducer 14b (the other movable unit) having a correlation with the abnormality detected in the speed reducer 14 a.

Hereinafter, the description will be made in more detail with reference to fig. 7A and 7B. Fig. 7A is a diagram showing an abnormality detected in the past and maintenance performed in the past in the reduction gears 14a and 14b, and the horizontal axis represents time from 1 week to several weeks. This is referred to as "maintenance history a". Fig. 7B is a diagram showing an abnormality detected in the past and maintenance performed in the past in the reduction gears 14a and 14B, and the horizontal axis represents a period of 1 year to several years. This is referred to as "maintenance history B". It is noted that ". Tra" in fig. 7A indicates an abnormality detected this time, and ". Terrific" in fig. 7B indicates that no abnormality is detected, i.e., normal. Further, ". Smallcircle" indicates abnormality, ". DELTA" indicates replacement of grease, "\9633;" indicates replacement of the speed reducer 14.

As shown in the maintenance history a of fig. 7A, an abnormality occurs in the reduction gear 14b at time t1, and then grease replacement is performed at time t 2. The present abnormality is detected at time t3 in the reduction gear 14 a.

On the other hand, as shown in the maintenance history B of fig. 7B, the reduction gear 14a is replaced at time t11, and the abnormality is detected at this time at time t12 (corresponding to t3 of fig. 7A) within 1 year after that.

Since the abnormality (t 3 in fig. 7A, t12 in fig. 7B) detected in the speed reducer 14a this time is within 1 year from the last replacement of the speed reducer 14a, the abnormality prediction unit 23 determines that the possibility of the abnormality occurring in the speed reducer 14a is low.

Further, grease replacement is performed on the speed reducer 14b at time t2 in fig. 7A. If the correlation stored in the correlation storage DB 34 is referred to, the reduction gear 14b is highly likely to have an abnormality. That is, referring to the maintenance history a shown in fig. 7A, since an abnormality is detected at time t1 and maintenance for replacing grease is performed at time t2 with respect to the reduction gear 14b, it is determined that there is a high possibility that an abnormality of such an extent that the reduction gear needs to be replaced occurs in the reduction gear 14b at time t 3. That is, it is determined that there is a high possibility that an abnormality has occurred in the speed reducer 14b and the speed reducer 14a, which operates in conjunction with the speed reducer 14b due to the occurrence of the abnormality, has an abnormality.

Therefore, even when an abnormality is detected in the speed reducer 14a, the abnormality prediction unit 23 determines that there is a high possibility that an abnormality has occurred in the speed reducer 14b operating in conjunction with the speed reducer 14a, and predicts an abnormality of the speed reducer 14b in addition to the abnormality of the speed reducer 14 a. That is, when an abnormality of the reduction gear 14a is detected, the abnormality prediction unit 23 refers to maintenance data for an abnormality of at least one other reduction gear 14 (reduction gear 14 b) and determines information on the abnormality of the reduction gear 14b.

The notification content setting unit 24 sets a content to be displayed on the display 62 of the user interface 103 and notified to the operator. A tree image 73 is generated in which the result of the abnormality diagnosis of the reduction gear 14 diagnosed as abnormal by the abnormality prediction unit 23 is expressed in a tree structure.

Fig. 8 is an explanatory diagram showing an example of the tree image 73. For example, when an abnormality is detected in the decelerator 14a, an image of an "abnormality diagnosis tree" is generated as shown in a block q1 of fig. 8. As shown in block q2, "abnormality degree 2.1> reference value 1.0" indicating that the value "2.1" of the abnormality degree calculated at this time is larger than the reference value "1.0" is generated. As described later, the image of the mark K1 is generated so that the correspondence with the waveform of the degree of abnormality can be easily recognized. Note that the "symbol" described in this embodiment is a concept including characters, predetermined marks, icons, and the like.

As shown in block q3, an image of "maintenance history B" and "reduction gear 14a replaced within 1 year" is generated. As shown in block q4, "maintenance history a", "decelerator 14b: the image of "needs to be noted.

Then, an image indicating the content of the maintenance performed for the abnormality is generated. In this case, since it is assumed that an abnormality has occurred in the reduction gear 14a or the reduction gear 14b, an image showing that maintenance "please measure the iron powder concentration of grease in the reduction gear 14a or the reduction gear 14b" is performed is generated as shown by block q 5. The tree image 73 includes maintenance commands for the reduction gear 14a and the reduction gear 14b predicted to have an abnormality, and the relationship between the occurrence of the abnormality and the maintenance commands is associated.

Display frames of blocks q1 to q5 showing a flow from occurrence of an abnormality to execution of maintenance are displayed by bold lines. Display frames of the other blocks q6, q7, q8 are displayed by thin lines. Therefore, when an abnormality is detected in the reduction gear 14a, the operator can systematically recognize the passage until the abnormality of the reduction gear 14b is predicted in the abnormality prediction unit 23. Further, the blocks q1 to q5 and the blocks q6 to q8 may be displayed in a color-divided manner or highlighted by a hatching or the like.

That is, when the abnormality of the speed reducer 14a is detected, the notification content setting unit 24 generates a tree image 73 (see fig. 9) in which the information of the speed reducer 14a and the information of the speed reducer 14b (blocks q4 and q6 in fig. 8 and 9) are displayed in a tree structure. Then, a tree image 73 in which the display form (for example, the thickness of the line) of the information of the reduction gear 14b is changed in accordance with the content of the abnormality prediction of the reduction gear 14b is generated for the information of the reduction gear 14b.

The notification content setting unit 24 generates the tree image 73, the abnormality degree display image 71 showing the waveform of the disturbance torque and the waveform of the abnormality degree shown in fig. 5, and the maintenance history image 72 shown in fig. 7A and 7B. The maintenance history image 72 includes an image (first maintenance history image) indicating the maintenance history a and an image (second maintenance history image) indicating the maintenance history B whose scale of the time axis is different from that of the maintenance history a. The generated

images

71, 72, and 73 are combined to generate an abnormality diagnostic image 70 shown in fig. 9. Further, the same operation instructions as those of the blocks q5, q7, and q8 shown in fig. 8 are shown in the blocks q5, q7, and q8 shown in fig. 9.

Here, as shown in fig. 2, the abnormality diagnostic device 102 can also be realized by using a computer including a CPU 41 (central processing unit), a memory 42, and databases (a sensor DB 31, a maintenance history DB32, an abnormality prediction DB 33, a correlation storage DB 34, and a work history DB 35). A computer program (abnormality diagnostic program) for causing a computer to function as the abnormality diagnostic device 102 is installed in the computer and executed. Thus, the CPU 41 functions as a plurality of information processing circuits provided in the abnormality diagnostic device 102, that is, the communication unit 21, the abnormality degree determination unit 22, the abnormality prediction unit 23, the notification content setting unit 24, and the correlation analysis unit 25.

The above-described functions of the abnormality diagnostic device 102 can be realized by one or more processing circuits. The processing circuit includes a programmed processing device such as a processing device including an electrical circuit. The processing device further includes a device such as an Application Specific Integrated Circuit (ASIC) or an existing circuit component arranged to execute the functions provided by the abnormality diagnostic device 102.

[ description of operation of the first embodiment ]

Next, the operation of the abnormality diagnostic device 102 according to the first embodiment will be described with reference to flowcharts shown in fig. 3 and 4. Fig. 3 is a flowchart showing a processing procedure of the correlation analysis process performed by the correlation analysis section 25.

First, in step S11, the correlation analysis unit 25 acquires maintenance data performed in each reduction gear 14 from the maintenance history DB 32.

In step S12, the correlation analysis unit 25 performs correlation analysis based on the maintenance data performed in the plurality of reduction gears 14. For example, as shown in fig. 6, since an abnormality of the speed reducer 14 is detected (indicated by "o" in the figure), then a grease replacement is performed (indicated by "Δ" in the figure), and then a maintenance operation for replacing the speed reducer 14 is performed at a high frequency (indicated by "9633;", in the figure), it is determined that there is a correlation between the grease replacement and the subsequent replacement of the speed reducer 14.

In step S13, the correlation analysis unit 25 stores the correlation obtained by the correlation analysis in the correlation storage DB 34, and ends the present process.

Fig. 4 is a flowchart showing a processing procedure of the abnormality diagnosis processing. First, in step S31, the abnormality degree determination unit 22 acquires disturbance torque of a certain reduction gear (here, the reduction gear 14 a) from the sensor DB 31. As a result, time-series data of the disturbance torque is obtained as shown in fig. 5 (a), for example.

In step S32, the abnormality degree determination unit 22 calculates the abnormality degree indicating the degree of abnormality of the reduction gear 14a based on the acquired disturbance torque by using the above equation (1). As a result, time-series data of the degree of abnormality is obtained as shown in fig. 5 (b), for example.

In step S33, the abnormality degree determination unit 22 determines whether or not an abnormality has occurred in the reduction gear 14a based on the abnormality degree calculated in the process of step S32. For example, a reference value of the degree of abnormality is set to 1.0, and when the calculated degree of abnormality exceeds the reference value, it is determined that the reduction gear 14a is abnormal. In the example shown in fig. 5 (b), the abnormality degree exceeds the reference value at the time point indicated by the symbol p1, and thus it is determined to be abnormal.

If an abnormality has not occurred in the decelerator 14a (no in step S33), the present process is ended. That is, when no abnormality occurs in all the reducers 14, the notification content setting unit 24 does not display information relating to the abnormality. Only when it is diagnosed that an abnormality has occurred in at least one of the reducers 14, information about the abnormality is displayed on the display 62.

In step S34, the notification content setting unit 24 generates display data of the disturbance torque and the abnormality degree displayed on the display 62 of the user interface 103. As a result, display data of the abnormality degree display image 71 shown in fig. 9 is generated.

In step S35, the abnormality prediction unit 23 acquires a list of the speed reducers 14 that operate in conjunction with the speed reducer 14a based on the information stored in the correlation storage DB 24. As a result, the reducer 14 operating in conjunction with the reducer 14a, for example, the reducer 14b is determined.

In step S36, the abnormality prediction unit 23 acquires maintenance data of the reduction gears 14a and 14b from the maintenance history DB 32.

In step S37, the notification content setting unit 24 generates display data of the maintenance history a (see fig. 7A) and the maintenance history B (see fig. 7B) to be displayed on the display 62. As a result, display data of the maintenance history image 72 shown in fig. 9 is generated.

In step S38, the abnormality prediction unit 23 refers to the maintenance history B. Then, in step S39, it is determined whether or not the abnormality detected in the reduction gear 14a contradicts the maintenance data of the maintenance history B. If it is determined that there is a conflict (yes in step S39), the process proceeds to step S40, and if it is not determined that there is a conflict (no in step S39), the process proceeds to step S43.

In the maintenance history B shown in fig. 7B, the reduction gear 14a is replaced at time t11, and the current abnormality detected at time t12 is within 1 year from time t11, so that the reduction gear 14a is less likely to be abnormal. Therefore, it is determined that the abnormality detected in the reduction gear 14a contradicts the maintenance data of the maintenance history B (yes in step S39).

In step S40, the abnormality prediction unit 23 refers to the maintenance history a, and in step S41, determines whether or not there is maintenance data indicating an abnormality in the reduction gear 14b. If there is maintenance data indicating an abnormality (yes in step S41), the process proceeds to step S42, and if there is no maintenance data indicating an abnormality (no in step S41), the process proceeds to step S43.

In the example shown in fig. 7A, since an abnormality of the speed reducer 14b is detected at time t1 and the grease is replaced at time t2, there is a high possibility that an abnormality requiring replacement of the speed reducer 14b occurs at time t3 thereafter. Therefore, it is determined that there is maintenance data indicating an abnormality (yes in step S41).

In step S42, the abnormality prediction unit 23 determines an instruction to measure the iron powder concentration in the

speed reducers

14a and 14b.

In step S43, the abnormality prediction unit 23 determines an instruction for measuring the iron powder concentration in the reduction gear 14 a.

In step S44, the notification content setting unit 24 generates display data of the tree image 73 shown in fig. 8. The tree image 73 is set as follows: when an abnormality is detected in the reduction gear 14a, the operator can easily recognize the passage of the work content instructed to the operator in response to the abnormality detection. Then, display data of an abnormality degree display image 71 showing the disturbance torque and the abnormality degree shown in fig. 5 and display data of a maintenance history image 72 shown in fig. 7A and 7B are generated, and display data of an abnormality diagnostic image 70 shown in fig. 9 is generated.

As shown in fig. 9, the abnormality degree display image 71 displays a mark K1 (a mark of a clip), and also displays "abnormality degree 2.1> reference value 1.0". The same symbol K1 and characters are displayed in the block q2 of the tree image 73. That is, the abnormality diagnostic image 70 including the tree image 73 and the abnormality degree display image 71 is generated so that the same mark as the mark shown in the abnormality degree display image 71 is attached to the corresponding information in the tree image 73. Therefore, the correspondence relationship between the tree image 73 and the abnormality degree display image 71 can be displayed in a manner easily recognizable.

On the other hand, the mark K2 is displayed in the maintenance history image 72, and "the reduction gear 14b: attention is required. The same symbol K2 and characters are displayed in the block q4 of the tree image 73. That is, the abnormality diagnostic image 70 including the tree image 73 and the maintenance history image 72 is generated so that the same symbol as the symbol shown in the maintenance history image 72 is attached to the corresponding information in the tree image 73. Therefore, the correspondence relationship between the tree image 73 and the maintenance history image 72 can be displayed so as to be easily recognized.

In step S45, the notification content setting unit 24 outputs the display data of the abnormality diagnostic image 70 shown in fig. 9 to the user interface 103. After that, the present process is ended.

The display control unit 61 of the user interface 103 receives the display data of the abnormality diagnostic image 70 and displays the abnormality diagnostic image 70 shown in fig. 9 on the display 62. The operator can recognize the following by observing the image displayed on the display 62: the disturbance torque of the reduction gear 14a greatly fluctuates and an abnormality is detected, and there is a possibility that an abnormality occurs in the reduction gear 14b that operates in association with the reduction gear 14 a.

As described above, the abnormality diagnostic device 102 according to the first embodiment can achieve the following effects.

(1)

When an abnormality of the speed reducer 14a is detected based on disturbance torque (data on the state of the movable portion) of the speed reducer 14a (one movable portion), an abnormality of the speed reducer 14b (the other movable portion) that operates in conjunction with the speed reducer 14a is predicted, and information on the abnormality is displayed on the display 62. Therefore, it is possible to notify the operator of not only the abnormality of the reduction gear 14a in which the abnormality is detected but also the abnormality of the reduction gear 14b in which the abnormality is likely to occur in association with the abnormality detected in the reduction gear 14a, and it is possible to perform the abnormality diagnosis of the reduction gear 14 in a wide range.

(2)

The abnormality prediction unit 23 determines information relating to an abnormality of the reduction gear 14 based on the maintenance data stored in the maintenance history DB32, the timing of maintenance performed in each reduction gear 14, the contents of maintenance such as replacement of grease or replacement of the reduction gear 14. Therefore, the abnormality diagnosis of the reducer 14 can be performed with higher accuracy.

(3)

When an abnormality of the speed reducer 14a is detected, the information on the abnormality of the speed reducer 14b is determined with reference to the maintenance data of the speed reducer 14b that operates in conjunction with the speed reducer 14 a. Therefore, the abnormality diagnosis of the reducer 14 can be performed with high accuracy.

(4)

The correlation analysis unit 25 performs correlation analysis based on the abnormality occurring in each reduction gear 14 and the contents of maintenance performed in each reduction gear 14, and stores the correlation of each reduction gear 14 in the correlation storage DB 34. Then, when an abnormality is detected in the reduction gear 14a based on the correlation stored in the correlation storage DB 34, an abnormality of the reduction gear 14b having a correlation with the abnormality occurring in the reduction gear 14a is predicted. Therefore, it is possible to perform a highly accurate abnormality diagnosis in accordance with the correlation between the abnormality occurring in each reduction gear 14 and the maintenance performed in each reduction gear 14.

(5)

When an abnormality is detected in the decelerator 14a, display data of the tree image 73 shown in fig. 9 is generated. Then, the flow from the occurrence of an abnormality to the execution of maintenance is highlighted in the tree image 73. Specifically, the frames of blocks q1 to q5 shown in fig. 9 are displayed in bold lines. Therefore, the operator can systematically recognize the passage from the occurrence of an abnormality to the execution of maintenance by observing the tree image 73 for abnormality diagnosis. The abnormality diagnostic image 70 can be displayed in a manner easily recognizable by even an operator who does not have expert knowledge about the robot 101. Further, the highlight display may be performed by a shadow display, a display color, or the like.

(6)

As shown in fig. 9, the symbol K2 displayed in the maintenance history image 72 and the symbol K2 displayed in the corresponding portion (block q 4) of the abnormality diagnostic tree are set to be the same symbol. The operator can associate the tree image 73 with the content to be identified by the same symbol. The display can be made in a manner that is easier for the operator to understand.

(7)

As shown in fig. 9, the sign K1 displayed in the abnormality degree display image 71 indicating the disturbance torque and the abnormality degree is the same as the sign K1 displayed in the corresponding portion (block q 2) of the abnormality diagnostic tree. The operator can associate the tree image 73 with the content to be identified by the same symbol. The display can be made in a manner that is easier for the operator to understand.

(8)

As shown in fig. 9, in the tree image 73, the abnormality occurring in the reduction gear 14a is displayed in association with the contents of the maintenance performed in the reduction gear 14b, which is predicted to have the abnormality. The operator can be notified of the contents of maintenance that should be performed in association with the abnormality occurring in the reduction gear 14a in a manner that is more easily recognizable to the operator (blocks q5, q7, q 8).

(9)

The notification content setting unit 24 outputs a diagnostic image display command shown in fig. 9 only when an abnormality occurs in at least one of the reducers 14. Therefore, unnecessary display can be omitted. In addition, since the abnormality diagnostic image 70 is not displayed when an abnormality has not occurred, it is possible to suppress the occurrence of a problem of misrecognizing whether an abnormality has occurred.

[ description of modification of the first embodiment ]

Next, a modified example of the first embodiment will be described. In the modification, of the maintenance history a and the maintenance history B displayed as the maintenance history image 72, the maintenance history that is the basis for predicting the abnormality of the reduction gear 14B is displayed in an enlarged or emphasized manner. That is, two maintenance histories, namely, a maintenance history a and a maintenance history B, having different scales on the time axis are displayed, and an image of the maintenance history, which is a basis of prediction of occurrence of an abnormality, is displayed in an enlarged or highlighted manner. For example, as shown in fig. 10, the maintenance history a is displayed enlarged compared to the maintenance history B. Therefore, the operator can be notified of the basis of the determination that the reduction gear 14b is abnormal in a more easily recognizable manner.

[ description of the second embodiment ]

Next, a second embodiment of the present invention will be explained. The device configuration of the abnormality diagnostic device according to the second embodiment is the same as the configuration of fig. 1 described above. The second embodiment is different from the first embodiment described above in that the correlation analysis unit 25 shown in fig. 1 performs machine learning and generates a learning model based on the abnormality data detected in the past in each reduction gear 14 and the maintenance history performed. That is, the correlation analysis unit 25 performs the following machine learning: the pattern of the maintenance data having a high possibility of occurrence of an abnormality is learned based on the maintenance data of each reduction gear 14 stored in the maintenance history DB32 for at least a part of the period. Then, abnormality of the decelerator 14 is detected based on the result of the machine learning.

In the machine learning, the regularity of past abnormal data and maintenance data included in the maintenance history is extracted to generate a learning model. As a method therefor, for example, a known "supervised learning" can be used.

Regarding "supervised learning", a large number of pieces of abnormality data detected in the past and maintenance data implemented are acquired, and a learning model is generated from a combination of these pieces of data, an occurrence order, and an occurrence time interval. Fig. 11 is a time chart showing maintenance (H1 to Hn) performed in each reduction gear 14 and an abnormality detected in the reduction gear 14. As shown in fig. 11, machine learning is performed based on the relationship between the contents (reference numeral 201) of various kinds of maintenance (H1 to Hn) performed on each reduction gear 14 before the past time t21 (for example, 1 month ago) and the abnormal data generated in each reduction gear 14, and a learning model is generated. The generated learning model is stored into the correlation storage DB 34.

In addition, as a method other than supervised learning, a learning model can be generated using "unsupervised learning". In addition, as a method other than machine learning, a learning model can be generated using deep learning.

When an abnormality is detected in a certain reduction gear 14, the abnormality prediction unit 23 refers to the learning model stored in the correlation storage DB 34 to extract another reduction gear 14 having a high possibility of abnormality occurrence.

The abnormality diagnostic device according to the second embodiment is the same as the first embodiment described above, except that a learning model is used, and therefore, the description of the processing procedure is omitted.

As described above, in the abnormality diagnosis device according to the second embodiment, the learning model is generated by using the machine learning method based on the maintenance data performed in the past in each reduction gear 14. When an abnormality occurs in the reducer 14a, the abnormality occurring in the reducer 14b is predicted with reference to the learning model described above. Therefore, the abnormality diagnosis of the reducer 14 can be performed with higher accuracy.

Note that the device to be diagnosed for an abnormality is not limited to the robot 101. For example, instead of the motor, an engine of an automobile may be used, and instead of the reducer 14, a transmission may be used. In addition, it is also possible to target all devices including a rotating mechanism of a moving body, a moving body such as a game machine of an amusement park, a machine tool such as a three-dimensional printer, and the like, and a mechanism for transmitting the rotation. In addition, other types of devices may be targeted.

Further, the abnormality diagnosis device may be disposed at a remote location, and may determine an abnormality of the equipment by transmitting and receiving a necessary signal or data via a communication line. In addition, one abnormality diagnosis apparatus may be used to diagnose abnormality in a plurality of devices. The plurality of devices may be disposed at different locations.

The above description is of the embodiments of the present invention, but it should not be understood that the description and drawings constituting a part of the present disclosure are intended to limit the present invention. Various alternative embodiments, examples, and techniques of use will be apparent to those skilled in the art in light of this disclosure.

Description of the reference numerals

11: a communication unit; 12: a disturbance torque calculation unit; 13: a sensor; 14: a speed reducer; 15: an operation control unit; 21: a communication unit (control unit); 22: an abnormality degree determination unit (control unit); 23: an abnormality prediction unit (control unit); 24: a notification content setting unit (control unit); 25: a correlation analysis unit (control unit); 31: a sensor DB;32: a maintenance history DB (maintenance history storage unit); 33: an anomaly prediction DB;34: a correlation storage DB (correlation storage unit); 61: a display control unit; 62: a display (display unit); 101: a robot; 102: an abnormality diagnostic device; 103: a User Interface (UI).

Claims

1. An abnormality diagnostic device that diagnoses an abnormality of each movable portion based on movable portion data regarding a state of each movable portion acquired from a sensor provided in an apparatus having a plurality of movable portions, and outputs a result of the diagnosis to a display portion, the abnormality diagnostic device comprising:

a maintenance history storage unit that stores maintenance data relating to maintenance performed on each of the movable units; and

a control unit that detects an abnormality of each of the movable units based on the movable unit data,

wherein the control unit predicts an abnormality of another movable unit occurring in association with the abnormality of the one movable unit based on the maintenance data when the abnormality of the one movable unit is detected based on the movable unit data, and outputs information on the predicted abnormality of the another movable unit to the display unit when the abnormality of the another movable unit is predicted.

2. The abnormality diagnostic device according to claim 1,

the control unit determines information on an abnormality of the other movable unit based on the timing of maintenance performed in each of the movable units and the content of the maintenance in the maintenance data stored in the maintenance history storage unit.

3. The abnormality diagnostic device according to claim 1 or 2,

the control unit determines information on an abnormality of the other movable unit with reference to maintenance data for an abnormality of at least one other movable unit when an abnormality of the one movable unit is detected.

4. The abnormality diagnostic device according to claim 1 or 2,

further comprises a correlation storage unit for storing a correlation between an abnormality occurring in the one movable unit and an abnormality occurring in the other movable unit,

when an abnormality is detected in the one movable unit, the control unit refers to the correlation stored in the correlation storage unit to predict an abnormality of another movable unit having a correlation with the abnormality detected in the one movable unit.

5. The abnormality diagnostic device according to claim 1 or 2,

the control unit performs machine learning, which is a pattern of learning maintenance data having a high possibility of occurrence of an abnormality, based on maintenance data of each of the movable units stored in the maintenance history storage unit for at least a part of the period, and detects an abnormality of the other movable units based on a result of the machine learning.

6. The abnormality diagnostic device according to claim 1 or 2,

the control unit generates a tree image in which information on the one movable unit and information on the other movable units are expressed in a tree structure when an abnormality of the one movable unit is detected,

the control unit changes the display mode of the information of the other movable unit and outputs the changed information to the display unit according to the content of the abnormality prediction of the other movable unit.

7. The abnormality diagnostic device according to claim 6,

the control unit generates not only the tree image but also a maintenance history image showing time-series maintenance data of the one movable unit and at least one other movable unit,

when an abnormality of the other movable unit is predicted, the control unit outputs the tree image and the maintenance history image to the display unit so that a symbol indicating maintenance data that is a basis of the prediction is attached to the maintenance history image and the same symbol as the symbol is attached to information of the other movable unit in the tree image.

8. The abnormality diagnostic device according to claim 7,

the control section generates a first maintenance history image and a second maintenance history image as the maintenance history image, a scale of a time axis of the second maintenance history image being different from a scale of a time axis of the first maintenance history image,

the control unit outputs an image including maintenance data, which is a basis for predicting an abnormality of the other movable unit, of the first maintenance history image and the second maintenance history image to the display unit after emphasizing or enlarging the image.

9. The abnormality diagnostic device according to claim 6,

the control unit calculates an abnormality degree indicating a degree of abnormality occurring in the movable unit data,

the control unit generates not only the tree image but also a maintenance history image including the movable unit data, an abnormality degree display image of the abnormality degree, and maintenance data representing a time series of the one movable unit and at least one other movable unit,

when an abnormality of the other movable unit is predicted, the control unit outputs the tree image and the maintenance history image to the display unit so that a mark indicating the degree of abnormality that is a basis of the prediction is attached to the abnormality degree display image and the same mark as the mark is attached to information of the other movable unit in the tree image.

10. The abnormality diagnostic device according to claim 6,

the tree image includes maintenance instructions for the one movable unit and other movable units predicted to have an abnormality, and the tree image is associated with a relationship between the occurrence of the abnormality and the maintenance instructions.

11. The abnormality diagnostic device according to claim 1 or 2,

the control unit outputs information on an abnormality to the display unit only when it is diagnosed that an abnormality has occurred in at least one of the movable units.

12. An abnormality diagnostic method of diagnosing an abnormality of each movable portion based on movable portion data on a state of each movable portion acquired from a sensor provided in an apparatus having a plurality of movable portions, and outputting a result of the diagnosis to a display portion, the abnormality diagnostic method characterized in that,

when an abnormality of one movable unit is detected based on the movable unit data, an abnormality of another movable unit occurring in association with the abnormality of the one movable unit is predicted based on maintenance data relating to maintenance performed on each movable unit, and when an abnormality of the other movable unit is predicted, information relating to the predicted abnormality of the other movable unit is output to the display unit.